np_boot_ci: Non-parametric cluster bootstrap to make inference on the...

In cccrm: Concordance Correlation Coefficient for Repeated (and Non-Repeated) Measures

Non-parametric cluster bootstrap to make inference on the concordance correlation coefficient

Description

Non-parametric cluster bootstrap to make inference on the concordance correlation coefficient

Usage

np_boot_ci(
  dataset,
  ry,
  rind,
  rtime = NULL,
  rmet = NULL,
  vecD = NULL,
  covar = NULL,
  rho = 0,
  int = FALSE,
  cl = 0.95,
  control.lme = list(),
  nboot = 500,
  parallel = TRUE,
  future_seed = 123
)

Arguments

dataset	an object of class data.frame
ry	Character string. Name of the outcome in the data set.
rind	Character string. Name of the subject variable in the data set.
rtime	Character string. Name of the time variable in the data set.
rmet	Character string. Name of the method variable in the data set.
vecD	Vector of weights. The length of the vector must be the same as the number of repeated measures.
covar	Character vector. Name of covariates to include in the linear mixed model as fixed effects.
rho	Within subject correlation structure. A value of 0 (default option) stands for compound symmetry and 1 is used for autorregressive of order 1 structure.
int	Binary indicating if the subject-method interaction has to be included in the model when analyzing the non-longitudinal setting (defaults to FALSE).
cl	Confidence level.
control.lme	A list of control values for the estimation algorithm used in lme function. For further details see lme help.
nboot	Number of bootstrap resamples.
parallel	Whether the code is parallellized. The parallellization method is multisession.
future_seed	Logical/Integer. The seed to be used for parallellization. Further details in furrr_options.

Value

Vector of CCC bootstrap estimates.

cccrm documentation built on Oct. 19, 2024, 9:06 a.m.